Content-based analysis to detect Arabic web spam
- 19 April 2012
- journal article
- research article
- Published by SAGE Publications in Journal of Information Science
- Vol. 38 (3), 284-296
- https://doi.org/10.1177/0165551512439173
Abstract
Search engines are important outlets for information query and retrieval. They have to deal with the continual increase of information available on the web, and provide users with convenient access to such huge amounts of information. Furthermore, with this huge amount of information, a more complex challenge that continuously gets more and more difficult to illuminate is the spam in web pages. For several reasons, web spammers try to intrude in the search results and inject artificially biased results in favour of their websites or pages. Spam pages are added to the internet on a daily basis, thus making it difficult for search engines to keep up with the fast-growing and dynamic nature of the web, especially since spammers tend to add more keywords to their websites to deceive the search engines and increase the rank of their pages. In this research, we have investigated four different classification algorithms (naïve Bayes, decision tree, SVM and K-NN) to detect Arabic web spam pages, based on content. The three groups of datasets used, with 1%, 15% and 50% spam contents, were collected using a crawler that was customized for this study. Spam pages were classified manually. Different tests and comparisons have revealed that the Decision Tree was the best classifier for this purpose.Keywords
This publication has 21 references indexed in Scilit:
- Evaluating Google queries based on language preferencesJournal of Information Science, 2011
- The Automatic Evaluation of Website Metrics and StateInternational Journal of Web-Based Learning and Teaching Technologies, 2010
- Using evidence based content trust model for spam detectionExpert Systems with Applications, 2010
- Learning to Detect Web Spam by Genetic ProgrammingLecture Notes in Computer Science, 2010
- Identifying Spam Web Pages Based on Content SimilarityLecture Notes in Computer Science, 2008
- Content Trust Model for Detecting Web SpamPublished by Springer Science and Business Media LLC ,2007
- Improving web spam classification using rank-time featuresPublished by Association for Computing Machinery (ACM) ,2007
- EviRank: An Evidence Based Content Trust Model for Web Spam DetectionLecture Notes in Computer Science, 2007
- Spam, damn spam, and statisticsPublished by Association for Computing Machinery (ACM) ,2004
- A large-scale study of the evolution of web pagesPublished by Association for Computing Machinery (ACM) ,2003